- 900 startups by [[alex_mitchell]] in [airtable](https://airtable.com/appFbQ1hCbix7FfhQ/tblknhbkg2zAya4IO/viwyD8YTLeZWly61T?blocks=show)
### **Top-Tier Matches**
|Rank|Organization|Rationale|
|---|---|---|
|1|**Tesla Motors**|Tesla exemplifies both criteria. Elon Musk made major early-stage R&D bets across energy storage, software, and manufacturing when the market for EVs was highly uncertain. Tesla's strategy reflected deep uncertainty across infrastructure, policy, and technology (e.g., proprietary charging vs CCS) and eventually required course correction when regulatory frameworks shifted. Tesla also delayed formal AV rollout to avoid premature lock-in.|
|2|**Uber**|Uber operated under extreme uncertainty in policy, operations, and platform design. It leveraged feedback loops and staged testing in multiple cities, often piloting before regulation existed. Founders didnāt know how adoption, pricing, or city collaboration would evolveāmaking probabilistic reasoning (like city-by-city viability testing) crucial. It fits the model of experimenting with different mobility layers (rideshare, Uber Eats, AV bets).|
|3|**SpaceX**|Though primarily aerospace, SpaceX overlaps with mobility (e.g., Hyperloop concepts, Starship logistics) and represents large bets under radical uncertainty. Muskās decision-making process involved deep probabilistic evaluations of tech-readiness levels, funding paths, and system dependencies. It invests in long-term platforms before policy consensus, aligning with the idea of ecosystem-first thinking.|
|Rank|Organization|Rationale|
|---|---|---|
|4|**Ford**|Ford straddles legacy manufacturing and open innovation. The company spearheaded Michigan Central (an open mobility R&D campus), aligning with collective infrastructure investment without early lock-in. Bill Ford explicitly chose not to predefine the type of mobility tech that would win out. However, the company as a whole sometimes leans toward incremental innovation, limiting its probabilistic openness at scale.|
|5|**Superpedestrian**|This micromobility startup emerged from uncertain city permitting, regulatory backlash (post-Scooter chaos), and infrastructure deficits. The company emphasized reliability and adaptive deployment based on probabilistic assessment of user safety and regulatory dynamics. It isnāt cited in transcript but fits the broader model.|
|6|**Lyft**|Lyft, like Uber, navigated uncertain regulatory terrain and experimented with multimodal logistics, AV partnerships, and e-bike expansions. However, it was more risk-averse and less aggressive in early-stage R&D compared to Uber. Still, Lyft helped shape shared mobility ecosystems and demonstrated adaptive market entry.|
| Organization | Why Lower Ranked |
| ------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Avis / Zipcar** | Zipcar did represent early experimentation in shared mobility, but was later absorbed into Avis. Post-acquisition, the focus shifted toward integration rather than early-stage ecosystem building. |
| **Toyota** | Strong internal R&D, but traditionally cautious and less ecosystem-driven in early AV/EV bets. Increasing experimentation recently (e.g., Woven Planet), but historically more deterministic. |
| **American Airlines / United / Southwest** | Important mobility players but innovation is more operational than R&D-infrastructure level. Rarely serve as open experimentation platforms for others. |
| **Segway** | Pioneered personal mobility but failed to engage broader innovation ecosystems or test systematically across segments. A cautionary tale of premature lock-in. |
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Would you like a visual version (e.g., 2x2 matrix) mapping these ventures along ecosystem openness Ć uncertainty tolerance?
2025-05-01
from [[scott_stern]]'s book
| Organization Name | Page Number(s) |
| ------------------------------------ | ----------------------------------------------------------------------- |
| Airbnb | 38, 78, 331, 356-57, 375, 444 |
| American Airlines | 226 |
| AutoWeb.com | 189 |
| Avis | 195 |
| Avis Budget Group | 204 |
| Boeing | 361 |
| Chrysler | 430 |
| Ford | 328 |
| Lowe's Travel Stops & Country Stores | 35 |
| Lyft | 131, 195, 220, 230 |
| Mastercard | 331 |
| Mobility Cooperative | 204 |
| Nokia | 141, 232 |
| Nordstrom | 189 |
| PayPal | 60, 236, 256, 315, 331, 341, 349, 444 |
| Pebble | 197, 197-98, 198, 199, 426 |
| Rent the Runway (RTR) | 13, 13, 292, 411 |
| Segway | 133, 142-44, 426 |
| Southwest Airlines | 223-24, 226, 227, 284 |
| SpaceX | 34, 35, 157, 359, 361, 361 |
| Superpedestrian | 181 |
| Tesla Motors | 34, 35, 85, 156, 157-59, 176, 179, 181, 345, 409-10 |
| Ticketmaster | 210, 434 |
| Toyota | 256, 257, 329 |
| TransferWise | 294, 294 |
| TravelPro | 160 |
| Uber | 32, 32, 72, 91, 92, 131, 195, 220, 230, 298-99, 301, 337, 375, 394, 431 |
| Uber Eats | 337 |
| ULine | 35 |
| United Airlines | 226 |
| U.S. Postal Service | 297 |
| Virgin Airlines | 284 |
| Zipcar | 204 |
Note: I've included transportation companies, payment systems essential for mobility services, and technology companies directly involved in mobility. Some entries may have multiple page numbers in italics, indicating figures on those pages.
I'll create a diagram that effectively showcases how probabilistic programming can benefit entrepreneurs in their decision-making process.
## How Probabilistic Programming Transforms Entrepreneurial Decision-Making
The diagram illustrates how probabilistic programming offers a powerful framework for entrepreneurial decision-making under uncertainty, addressing key limitations of traditional approaches.
### Key Benefits of the Probabilistic Programming Approach:
1. **Bayesian Reasoning vs. Intuition-Based Decisions**
Rather than relying solely on gut feelings, probabilistic programming enables entrepreneurs to formalize prior beliefs, update them with new evidence, and make decisions based on probability distributions rather than point estimates.
2. **Phase-Based Learning vs. Linear Planning**
Instead of rigid linear planning, the probabilistic approach adapts to different venture phases (nail, scale, sail), optimizing action sequences based on the specific uncertainty reduction needs of each phase.
3. **Hypothesis Networks vs. Single Business Model**
Probabilistic programming enables entrepreneurs to simultaneously maintain multiple business model hypotheses, evaluating them against evidence and stakeholder preferences systematically.
4. **Uncertainty Reduction vs. Imitative Behavior**
The framework shifts focus from imitating successful companies to systematically reducing uncertainties through the optimal sequencing of actions (Segment, Collaborate, Capitalize).
5. **Multi-Stakeholder Modeling vs. Complexity Overwhelm**
By formalizing stakeholder preferences (W) and their relation to uncertainties (U), entrepreneurs can navigate complex multi-stakeholder environments methodically rather than being overwhelmed.
### Practical Applications:
The Tesla/Better Place contrast demonstrates how different action sequences produce dramatically different outcomes. Tesla's success with SegmentāCollaborateāCapitalize versus Better Place's struggles with CollaborateāSegmentāCapitalize shows how probabilistic programming could help identify optimal action sequences before committing significant resources.
By systematically modeling how different decisions affect weighted uncertainties (WāUā + WāUā + WāUā), entrepreneurs can make better decisions, especially in complex multi-stakeholder environments like those faced by Uber.